Friday, 20 March 2020

CFA Institute Investment Foundations Program: Chapter 19 – Performance Valuation (Part II)

In a previous article, we introduced the CFA Institute Investment Foundation Program (Read more here).  It is a free program designed for anyone who wants to enter or advance within the investment management industry, including IT, operations, accounting, administration, and marketing.  Candidates who successfully pass the online exam earn the CFA Institute Investment Foundations Certificate.

There are total of 20 Chapters in 7 modules, covering all the essential topics in finance, economics, ethics and regulations.  This series of articles will highlight the core knowledge of each chapter.

Chapter 19 provides an overview of the performance valuation. The learning outcome of chapter 19 is as follows:

·       Describe a performance evaluation process;
·       Describe measures of return, including holding-period returns and time-weighted rates of return;
·       Compare use of arithmetic and geometric mean rates of returns in performance evaluation;
·       Describe measures of risk, including standard deviation and downside deviation;
·       Describe reward-to-risk ratios, including the Sharpe and Treynor ratios;
·       Describe uses of benchmarks and explain the selection of a benchmark;
·       Explain measures of relative performance, including tracking error and the information ratio;
·       Explain the concept of alpha;
·       Explain uses of performance attribution.

The calculation and analysis of reward-to-risk ratios allow an understanding of the price fund investors have to pay in terms of units of reward for each unit of risk—the total return—generated by the fund’s manager. All things being equal, a manager who produces a consistently high reward-to-risk ratio could be said to be more skilful than one who consistently produces a lower ratio. Investors who invest in a fund that is managed on an active rather than on a passive basis are effectively paying for the manager’s investment skill and expertise.

Fund manager skill is often referred to as alpha. Perhaps the best way to explain the concept of alpha is to consider the sources of a fund’s return, which is composed of three elements:
·       market return
·       luck
·       skill

Managers of passive investment funds aim to produce returns for investors. These managers, however, are not looking to add value to the portfolios by picking securities that they believe will outperform other securities. Instead, they typically buy and hold in the appropriate proportions those securities that comprise their benchmark. Although this process requires some skill, it is not so much investment skill as efficient administration. When the passive benchmark rises, the value of the passive fund tracking it should also rise; conversely, when the benchmark falls, the value of the passive fund should also fall. Therefore, over time, the fund should produce a return similar to that of the chosen benchmark minus fees.

Some of the return generated by an investment fund is the result of luck rather than judgement. The prices of financial assets held in portfolios are affected by events that cannot be foreseen by a fund manager.

Skilful fund managers may be unlucky on occasion and unskilled fund managers might enjoy some good luck. Because luck tends to even out over the long term, it is vital that investors are able to distinguish luck from skill. However, it is not always easy to do so.

A skilful fund manager is able to add value to a portfolio over and above changes to the portfolio’s value that are driven by market movements and that could have been produced by a passive fund manager.

Because luck will tend to even out over time, a skilful manager is one who adds this value consistently over time, year after year. This outperformance over the returns from a relevant market benchmark is generally referred to as alpha.

Performance evaluators try to distinguish between these three sources of fund manager return. To do so, factor models are used to determine the factors that make up returns and the importance of each factor. One such model is the capital asset pricing model (CAPM), from which the term alpha comes. This model includes a measure of systematic risk: beta. Systematic risk (also called market or non-diversifiable risk) is the risk that affects all risky investments and cannot be diversified away. Factor models, such as the CAPM, separate a fund’s performance into return from market performance (beta), from luck or randomness, or from the investment skills of the fund manager (alpha).

Benchmarks can also be used to explore the reasons for the fund manager’s performance. By using appropriate financial market indices, the fund manager’s performance can be decomposed to reveal the sources of returns. Depending on the nature of the fund, the performance itself might come from the following sources:
·       asset allocation
·       sector selection
·       stock selection
·       currency exposure

Knowing how a fund manager’s performance is derived is useful information both for the clients of the fund and for the investment management company. For example, if a fund manager is skilled at stock selection but less proficient at sector selection, another fund manager may be asked to give advice on the sector selection aspect of the portfolio, allowing the first fund manager to concentrate on stock selection. Knowing the strengths of fund managers can also help investors choose an investment fund.

Modern performance attribution software can allow investment management companies to drill down into the detail of a fund to reveal all of this performance information. By doing so, the company may conclude that a particular fund manager is very good at stock selection but weaker in sector selection. Given this information, the company might ask another manager with better sector selection skills to make sector-related decisions, allowing the first manager to continue to add value through picking stocks.

The consistent outperformance of an investment fund compared with its benchmark is best described as: free polls

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